Magnetic resonance imaging (MRI) can provide high-resolution structural and functional images with excellent soft-tissue contrast. It is widely used in diagnoses and treatments of various diseases. Accelerating MRI by under-sampling in the spatial-frequency domain (k-space) is commonly used to reduce motion-related artifacts and improve scan efficiency. Parallel imaging and compressed sensing (PICS) has been widely used to reconstruct under-sampled MR scans. However, these techniques may be computationally expensive for high spatial/temporal resolution acquisitions, resulting in significant delays in patient care.
Recently, supervised deep learning approaches have been applied to MRI reconstruction, and these approaches have been demonstrated to significantly improve the speed of reconstruction by parallelizing the computation and using a pre-trained neural network model. In these methods, it is necessary to collect sufficient ground-truth images to train the neural network. However, for many applications, ground-truth images are not available or extremely difficult to acquire. For example, in dynamic contrast-enhanced MR scans, image contrast is changing very rapidly after the injection of the contrast agent, which makes it impossible to acquire a fully-sampled k-space for a certain image contrast. In addition, for some applications, even when fully-sampled scans are achievable, it is clinically impractical to collect hundreds of these raw datasets, as each of them may take hours to acquire without PICS-based under-sampling.